Python FeaturePreprocessor示例

示例#1

class FeaturePreprocessorTest(unittest.TestCase):
    def setUp(self):
        self.path = 'D:/dane/'
        self.feature_preprocessor = FeaturePreprocessor(self.path)
        self.feature_preprocessor.apply()

    def test_number_of_organs(self):
        self.assertEqual(
            len(self.feature_preprocessor.list_with_features_object), 6)

    def test_get_features(self):
        dic = self.feature_preprocessor.get_feature()

    def test_get_classificator(self):
        dic = self.feature_preprocessor.get_data_for_classificator()

示例#2

文件： test_FeaturePreprocessor.py 项目： DanielGutmann/DICOM

class FeaturePreprocessorTest(unittest.TestCase):
    def setUp(self):
        self.path = 'D:/dane/'
        self.feature_preprocessor = FeaturePreprocessor(self.path)
        self.feature_preprocessor.apply()


    def test_number_of_organs(self):
        self.assertEqual(len(self.feature_preprocessor.list_with_features_object), 6)



    def test_get_features(self):
        dic = self.feature_preprocessor.get_feature()

    def test_get_classificator(self):
        dic = self.feature_preprocessor.get_data_for_classificator()

示例#3

文件： linearSVC.py 项目： IMAGE-ET/DICOM-4

__author__ = 'Agnieszka'
__author__ = 'Agnieszka'
from DataClassification.FeaturePreprocessor import FeaturePreprocessor

__author__ = 'Agnieszka'

import gc

__author__ = 'Agnieszka'

import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets, cross_validation

path = 'D:/dane/'
feature_preprocessor = FeaturePreprocessor(path)
feature_preprocessor.apply()
data, label = feature_preprocessor.get_data_for_classificator()
# import some data to play with

X = data[:, :-1]
# avoid this ugly slicing by using a two-dim dataset
y = data[:, -1]

h = .02  # step size in the mesh

# we create an instance of SVM and fit out data. We do not scale our
# data since we want to plot the support vectors
C = 1.0  # SVM regularization parameter
#svc = svm.SVC(kernel='linear', C=C)

示例#4

文件： test_FeaturePreprocessor.py 项目： DanielGutmann/DICOM

 def setUp(self):
     self.path = 'D:/dane/'
     self.feature_preprocessor = FeaturePreprocessor(self.path)
     self.feature_preprocessor.apply()

示例#5

文件： Classifiaction.py 项目： DanielGutmann/DICOM

from DataClassification.FeaturePreprocessor import FeaturePreprocessor

__author__ = 'Agnieszka'

import gc

__author__ = 'Agnieszka'


import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets, cross_validation

path = 'D:/dane/'
feature_preprocessor = FeaturePreprocessor(path)
feature_preprocessor.apply()
data,label=feature_preprocessor.get_data_for_classificator()
# import some data to play with

X = data[:,:-1]
                      # avoid this ugly slicing by using a two-dim dataset
y = data[:,-1]
X = X[y != 6]
y = y[y != 6]

h = .02  # step size in the mesh

# we create an instance of SVM and fit out data. We do not scale our
# data since we want to plot the support vectors
C = 1.0  # SVM regularization parameter
svc = svm.SVC(kernel='linear', C=C)

示例#6

 def setUp(self):
     self.path = 'D:/dane/'
     self.feature_preprocessor = FeaturePreprocessor(self.path)
     self.feature_preprocessor.apply()